Summary of Foundation Model Sherpas: Guiding Foundation Models Through Knowledge and Reasoning, by Debarun Bhattacharjya et al.
Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning
by Debarun Bhattacharjya, Junkyu Lee, Don Joven Agravante, Balaji Ganesan, Radu Marinescu
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey paper proposes a conceptual framework that guides the interaction between foundation models (FMs) and agents in achieving specific tasks. It highlights the limitations of current FMs, such as their self-supervised training and lack of trustworthiness, which hinder broader adoption. The authors categorize different agent roles, including updating the FM, assisting with prompting it, and evaluating its output. They also review state-of-the-art approaches that interact with FMs, emphasizing the nature and extent of involvement for each role. This framework aims to facilitate the effective use of FMs in practical AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can make better use of big language models like large language models. These models are very good at doing certain tasks, but they’re not perfect and sometimes don’t do what we want them to do. The problem is that these models are trained to just repeat what they learned from their training data, without really understanding what’s important or what we need. To fix this, the authors suggest different ways for us to work with these models, like giving them hints or helping them learn more. They also group existing ideas into categories, showing how each one works and what it can do. This will help us make better use of these powerful tools in real-world situations. |
Keywords
» Artificial intelligence » Prompting » Self supervised
